Software tools utilizing artificial intelligence (AI) through machine learning (ML) are becoming increasingly vital in Materials Science and Engineering (MS&E), particularly materials design and development. However, these tools are not yet widely integrated into undergraduate materials engineering curricula. This paper presents a critical review of existing approaches to introducing ML concepts to undergraduate students in materials engineering. Although Pythonbased frameworks such as Jupyter Notebooks, Scikit-learn, and Google Colab have been developed, their adoption remains relatively limited. Programming complexity and the unclear role of materials engineers in these exercises are likely reasons for this limited uptake. We propose greater emphasis on materials engineering domain knowledge and structured material data to enhance the application of ML in solving materials engineering problems, as required in industry. Additionally, there is a shortage of readily accessible, suitable datasets and tools for teaching ML to undergraduate MS&E students with minimal computer science (coding) background. Hence, more openly available real-world datasets for a range of materials engineering problems that could be used across various years of study would be beneficial in increasing adoption. The introduction of user-friendly AI software tools which do not require coding, would likely facilitate their integration into the classroom. A comparison can be drawn to the increased prevalence of finite element modeling software in engineering education over recent decades. We aim to engage the community in dialogue to foster ideas and encourage the adoption of AI tools in materials design and development within modern materials engineering curricula.
Galos, J. L., & Kivy, M. B., & Grunenfelder, L., & Nomura, K., & Saal, J. E. (2025, June), A critical review of approaches to teaching artificial intelligence in undergraduate materials engineering Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . 10.18260/1-2–55354